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dc.contributor.authorBaritompa, WP
dc.contributor.authorDur, M
dc.contributor.authorHendrix, EMT
dc.contributor.authorNoakes, L
dc.contributor.authorPullan, WJ
dc.contributor.authorWood, GR
dc.contributor.editorP.M. Pardalos
dc.date.accessioned2017-05-03T13:58:21Z
dc.date.available2017-05-03T13:58:21Z
dc.date.issued2005
dc.date.modified2010-08-20T06:26:02Z
dc.identifier.issn0925-5001
dc.identifier.doi10.1007/s10898-004-9968-y
dc.identifier.urihttp://hdl.handle.net/10072/4283
dc.description.abstractLarge scale optimisation problems are frequently solved using stochastic methods. Such methods often generate points randomly in a search region in a neighbourhood of the current point, backtrack to get past barriers and employ a local optimiser. The aim of this paper is to explore how these algorithmic components should be used, given a particular objective function landscape. In a nutshell, we begin to provide rules for efficient travel, if we have some knowledge of the large or small scale geometry.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeNetherlands
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom579
dc.relation.ispartofpageto598
dc.relation.ispartofissue4
dc.relation.ispartofjournalJournal of Global Optimization
dc.relation.ispartofvolume31
dc.rights.retentionY
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchNumerical and computational mathematics
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode4903
dc.subject.fieldofresearchcode4613
dc.titleMatching Stochastic Algorithms to Objective Function Landscapes
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.date.issued2005
gro.hasfulltextNo Full Text
gro.griffith.authorPullan, Wayne J.


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